2. Vulnerable People or Places? Behavior influences disease Risk/outcome of behavior depends on location High Risk Area Risky Behavior Low Risk Area Risky Behavior
11. Purpose Place vulnerability of HIV/AIDS is under researched Texas Department of State Health Services (DSHS) Statewide Coordinated Statement of Need 2008 – 2010 has identified and targeted: Cross-cutting issues Barriers to Care Critical gaps The purpose of this research is to operationalize these three areas
13. Selected Cross Cutting Issues African Americans are disproportionately affected by HIV/AIDS The Texas Department of State Health Services (DSHS) Statewide Coordinated Statement of Need 2008 – 2010
14. Selected Barriers to Care Access to services, especially in rural areas is a barrier The Texas Department of State Health Services (DSHS) Statewide Coordinated Statement of Need 2008 – 2010
15. Selected Barriers to Care People recently released from incarceration have barriers in access to care and lower levels of treatment
16. Selected Critical Gaps Availability of safe affordable housing is limited Lack of available health care choices in non-urban service areas affects access to care The Texas Department of State Health Services (DSHS) Statewide Coordinated Statement of Need 2008 – 2010
17. Data Variables to measure Cross-Cutting Issues: Late Testers (% of newly diagnosed HIV/AIDS who were late testers in ZIP) Race (% of black population in ZIP) Variables to measure Barriers to Care: Poverty (% of 18+ population in poverty in ZIP) Access to HIV/AIDS Service Providers (provider located in ZIP) Own vehicle (% >16 own vehicle in ZIP) Prison Presence (prison located in ZIP) Variables to measure Critical Gaps Rent (median rent in $ in ZIP) Health Professional Shortage Area (is ZIP in HPSA)
18. Methods Standard OLS Regression with R-squared change Poisson Regression Model due to positively skewed distribution
19.
20. The proportion of late testers is significantly higher in locations with higher HIV/AIDS counts.
21.
22. Zip codes with HIV/STD service providers have high HIV/AIDS counts compared to those without.
29. HIV Counts per 10K < -2.5 Std. Dev. -2.5 - -1.5 Std. Dev. -1.5 - -0.50 Std. Dev. -0.50 - 0.50 Std. Dev. 0.50 - 1.5 Std. Dev. > 1.5 Std. Dev. Potential Future Use – Predicting Vulnerable Areas ZIP Code Predictor Index >20
30. Potential Future Use HIV/AIDS Counts 0.000000 - 54.000000 54.000001 - 164.000000 164.000001 - 395.000000 395.000001 - 907.000000 ZIP Code Predictor Index >20
31. Potential Future Use – Locating Unmet Need Legend Red = ZIP with Service Provider Highlight = Top 20% HIV ZIP
32. Future Research Ground truthing and qualitative approach Recognizing geographic differences Incorporating structural level variables Moving to specific measures of vulnerability, away from proxy measures Incorporating time Assessment of actual barriers for those areas that used the most recent SCSN survey tool in their last assessment. Conduct targeted assessments using multiple data gathering techniques to identify specific barriers to care.
33. Implications for HIV/STD Public Health Conclusions based on previous models have informed HIV/AIDS public health prevention and treatment policies on how best to identify vulnerable individuals or groups and get them basic prevention tools and access to treatment The model introduced here would improve on this by also identifying neighborhood characteristics that serve as a barrier to access for those areas needing prevention and treatment In the end, a model such as this may be useful in identifying vulnerable places not only in terms of unmet need but also allow for prediction of future vulnerable places
34. Acknowledgements Dr. Joseph Oppong & Dr. ChetanTiwari And the University of North Texas GIS & Public Health Research Group http://www.geog.unt.edu/gispublichealth
Editor's Notes
-When analyzing a disease, it is often obvious that behavior plays a vital part, and even the most vital component when dealing with a disease such as HIV/AIDS-While studies may find that behavior that is susceptible to contracting HIV/AIDS does not vary by groups (e.g. adolescent promiscuity produces a similar risk as middle-age promiscuity), these same studies ignore whether or not the risk of that behavior will vary dependent upon the area-Risky behavior in an area with high HIV/AIDS rate is, in fact, more risky than that same behavior in a low HIV/AIDS rate area.
-“Spatial distribution of individual correlates (and risk factors) of HIV do not explain the neighborhood and regional variation in HIV seroprevalence. Neighborhoods and regions accounted for approximately 14 and 6% of the total variation in HIV…Our study provides evidence for independent contextual variations in HIV, above and beyond that which can be ascribed to geographical variations in individual-level correlates and risk factors. We emphasize the need to adopt both a group-based and a place-based approach, as opposed to the dominant high-risk group approach”
-A substantial number (1/3) of People Living with HIV/AIDS (PLWHA) across Texas are diagnosed late in the progression of HIV disease
-Blacks had the highest number and rate of new infections every year from 2001 to 2005. The 2005 rate of new cases in Blacks (78 per 100,000) was approximately five times higher than the rate in Whites and Hispanics
Clients often do not know where to go to get the services they need, or what services are needed. Assessment data consistently rate access to transportation as a primary barrier to care. “Publicly funded HIV health care services in Texas are concentrated in larger cities and individuals living outside these communities must travel long distances to access needed care and services.”
-The availability of safe affordable housing. Key informant interview data suggest that discrimination in housing, along with reimbursement rates below fair market rents for housing, places clients into housing in high crime/low income areas, a phenomenon that may lead to substance abuse issues, crime, and other factors that are known to affect access and maintenance in care-The lack of available health care choices in non-urban service areas affects access to care, especially for specialty services. The providers that are in operation are often dependent on one funding source and vulnerable to fluctuations in funds. Key informants from urban areas cite this as a potential problem if there are a small number of providers and clients are not comfortable accessing services from any of them
More details on the methods can be discussed after the presentation
Previous studies have shown lack of affordable housing to be associated with HIV risk behavior (Aidala, Cross, Stall, Harre, & Sumartojo, 2005; Shubert & Bernstein, 2007; Aidala & Sumartojo, 2007). 43% of geographic variation is attributable to the factors included here.
Highlighted areas = ZIP index > 20
Highlightedareas = ZIP index > 20
Red = Service provider; highlighted = top 20% HIV ZIP
